Face tracking technology has been recently introduced into consumer digital cameras enabling a new generation of user tools for the analysis and management of image collections (see, e.g., http://www.fotonation.com/index.php?module=company.news&id=39, wherein the entire site www.fotoination.com is incorporated by reference. In earlier research, it had been concluded that it should be practical to employ face similarity measures as a useful tool for sorting and managing personal image collections (see, e.g., P. Corcoran, G. Costache, Automated sorting of consumer image collections using face and peripheral region image classifiers, Consumer Electronics, IEEE Transactions on Volume 51, Issue 3, August 2005 Page(s):747-754; G. Costache, R. Mulryan, E. Steinberg, P. Corcoran, In-camera person-indexing of digital images Consumer Electronics, 2006, ICCE '06. 2006 Digest of Technical Papers, International Conference on 7-11 Jan. 2006 Page(s):2; and P. Corcoran, and G. Costache, Automatic System for In-Camera Person Indexing of Digital Image Collections, Conference Proceedings, GSPx 2006, Santa Clara, Calif., October 2006, which are all hereby incorporated by reference). The techniques described in this research rely on the use of a reference image collection as a training set for PCA based analysis of face regions.
For example, it has been observed that when images are added to such a collection there is no immediate requirement for retraining of the PCA basis vectors and that results remain self consistent as long as the number of new images added is not greater than, approximately 20% of the number in the original image collection. Conventional wisdom on PCA analysis would suggest that as the number of new images added to a collection increases to certain percentage that it becomes necessary to retrain and obtain a new set of basis vectors.
This retraining process is both time consuming and also invalidates any stored PCA-based data from images that were previously analyzed. It would be far more efficient if we could find a means to transform face region data between different basis sets.
In addition, it has been suggested that it is possible to combine training data from two or more image collections to determine a common set of basis vectors without a need to retrain from the original face data. This approach has been developed from use of the “mean face” of an image collection to determine the variation between two different image collections.
The mean face is computed as the average face across the members of an image collection. Other faces are measured relative to the mean face. Initially, the mean face was used to measure how much an image collection had changed when new images were added to that collection. If mean face variation does not exceed more than a small percentage, it can be assumed that there is no need to recompute the eigenvectors and to re-project the data into another eigenspace. If, however, the variation is significant between the two collections, then the basis vectors are instead re-trained, and a new set of fundamental eigenvectors should be obtained. For a large image collection, this is both time consuming and inefficient as stored eigenface data is lost. It is thus desired to have an alternative approach to a complete retraining which is both effective and efficient.